Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties

Actuators for Intelligent Electric Vehicles

This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

Evolutionary multi-objective optimization using neural-based estimation of distribution algorithms

Ph.DDOCTOR OF PHILOSOPH

Design and Optimization of High-Torque Ferrite Assisted Synchronous Reluctance Motor

Vysokomomentový asistovaný synchronní reluktanční motor může být, soudě podle nízkého počtu publikovaných článků, stále považován za relativně málo prozkoumané téma výzkumu. Tato ale i další výhody, jako nízká výrobní cena a vysoká hustota výkonu poutají pozornost výzkumných pracovníků. Navzdory tomu, že tento druh motoru je zajímavější z pohledu konvenčních nebo vysokootáčkových aplikací, tak se i trakční aplikace dostávají do popředí s tím, jak jsou objevovány vlastnosti tohoto motoru. Tato práce se zaměřuje na návrh tohoto typu motoru pro pohon lodi, který je navržen aby dosahoval vysokého momentu při nízkých otáčkách. Aplikace je definována výkonem 55 kW při 150 otáčkách za minutu a použitím levných feritových magnetů s cílem nízké ceny motoru. Návrh motoru je úzce propojen s optimalizačními algoritmy aby bylo dosaženo co nejlepšího výkonu v daném objemu stroje. Navzdory tomu, že návrh samotný je velice zajímavým tématem, tak práce deklaruje další teze, které jsou rovněž zajímavé a důležité. Vzhledem k tomu, že je práce zaměřena i na optimalizaci, tak prvním cílem práce je porovnání různých optimalizačních metod. V této práci jsou nejenom že různé druhy optimalizačních algoritmů, samoorganizující migrující algoritmus a genetický algoritmus, porovnány, ale jsou zde porovnány i různé optimalizační metody. Metoda založená na definování preferenčního vektoru a ideální multi-objektivní metody jsou v rovněž v této práci srovnány. Tyto algoritmy jsou srovnány v případě více optimalizovaných parametrů. Dalším scénářem pro porovnání ideálních multi-objektivních algoritmů je ten s menším počtem parametrů. Posledním cílem práce je laboratorní měření navrženého optimalizovaného stroje, které rovněž představuje další set výzev v této práci, které jsou diskutovány v poslední kapitole této práce.The high-torque assisted synchronous reluctance machine could be still considered, based on the relatively low amount of publications, as a rather unknown area of research. This and other main advantages, such as low manufacturing cost and a higher torque density of this machine type are driving researchers interest. Even though this machine type has become more interesting in the conventional or high-speed applications, the area of traction applications is slowly getting forward as the machine capabilities are discovered. This thesis is serving just this purpose of developing the ship propulsion driving motor, that is capable of sustaining the high-torque at low-speed. The application is defined by the 55 kW at 150 rpm using the low- cost ferrite magnets aiming to lower the cost. The design will be closely tied with optimization algorithms to deliver the best possible performance in the given volume. However the design challenge being difficult task on its own, the thesis is declaring other goals within, that are still very interesting and important. Since the optimization is included in the design process, the first goal, concluding from the given topic is to compare various optimization methods. Not only the two different optimization algorithms, self-organizing migrating algorithm and genetic algorithm, will be compared in the thesis, but even two multi-objective optimization approaches will be compared as well. The preference based vector and ideal multi-objective optimization techniques comparison will be demonstrated in one optimization scenario with a higher amount of optimized parameters. Other demonstrated goal within the thesis is the comparison of ideal multi-objective optimization with a lower number of parameters. The last goal will be the measurement of the designed and optimized machine, that introduced variety of challenges itself and all of them will be discussed within the last chapter.

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

Annual Research Report, 2010-2011

Annual report of collaborative research projects of Old Dominion University faculty and students in partnership with business, industry and government.https://digitalcommons.odu.edu/or_researchreports/1000/thumbnail.jp

Air Force Institute of Technology Research Report 2014

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Advances on Smart Cities and Smart Buildings

Modern cities are facing the challenge of combining competitiveness at the global city scale and sustainable urban development to become smart cities. A smart city is a high-tech, intensive and advanced city that connects people, information, and city elements using new technologies in order to create a sustainable, greener city; competitive and innovative commerce; and an increased quality of life. This Special Issue collects the recent advancements in smart cities and covers different topics and aspects